The resulting reconstructions are discriminative and semantically rich, making FRN both simpler and more powerful than prior reconstruction-based approaches.
We validate these claims by demonstrating across-the-board superiority on four fine-grained few-shot classification datasets (CUB [1]}, Aircraft [2]}, meta-iNat and tiered meta-iNat [3]}) and two general few-shot recognition benchmarks (mini-ImageNet [4]} and tiered-ImageNet [5]}). These results hold for both shallow and deep network architectures (Conv-4 [6]}, [7]} and ResNet-12 [8]}, [7]}).
